Affiliations: Department of Information Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan 475004, China | E-mail: [email protected]
Correspondence:
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Corresponding author: Department of Information Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan 475004, China. E-mail: [email protected].
Abstract: In order to improve the recognition effect of laser images, this study designed an intelligent recognition method of laser images based on big data analysis technology. On the basis of setting up the laser holographic scanning device and parameters, the laser image is obtained by using the calibration method of vision system. In order to avoid the limitation of coordinate system in the process of laser image recognition, a rational function model with general attributes is constructed. Then, convolutional neural network is used to output the feature data of laser images, and Spark parallel support vector machine algorithm is used to complete the classification of laser images. Finally, the SVM classification model based on the big data analysis technology is constructed. The texture feature data can be input to quickly output the classification results of laser images, and then the intelligent classification and recognition of laser images can be realized according to the probability distribution. Experimental results show that this method can accurately identify the tiny features in laser images, and the recognition results have high peak signal-to-noise ratio and high recognition accuracy.
Keywords: Laser holographic scanning, visual calibration, laser image, texture features, image classification, probability distribution, support vector machine, rational function model
